: Graph Neural Networks have been used to learn physical simulations with great success [1]}. Recently, they have been used to learn solutions to PDEs [2]}, [1]}. MPNN-based GNNs coupled with an autoregressive strategy demonstrate a superior performance to FNO and are able to make long rollouts with the help of the "pushforward-trick" that only propagates gradient of the last computed frame.
